Modern BI and analytical architecture has evolved beyond traditional DW with elements such as a logical data warehouse, data lakes, distribution hubs, data catalogs, analytical sandboxes, and data science hubs, along with both self-service data preparation and BI. But achieving a business return on investment (ROI) with these approaches is fruitless without the right data and technical architectures. This workshop clarifies what you need to know regarding structured, unstructured, and semi-structured data; hybrid integration and data engineering; and analytical uses by casual info consumers, power users, and data scientists.

The technology categories with associated products to be discussed and positioned for use:

• Databases (relational, columnar, in-memory, NoSQL, and Hadoop distributions)
• Data, application cloud, and hybrid data integration along with data preparation
• Data virtualization
• Descriptive, diagnostic, predictive, and prescriptive analytics
• Traditional BI and reporting versus data discovery tools
• On-premise, cloud, or hybrid deployments

Architectural concepts discussed:

• Data structures and workflow
• Data engineering, data integration, data preparation, and data virtualization
• Business intelligence and advanced analytics
• Enabling technologies
• Enterprise information management and governance